DATA INVENTORY, WATERSHED CHARACTERIZATION & SUFFICIENT DATA

Michael Schramm

michael.schramm@ag.tamu.edu

TWRI, Texas A&M AgriLife Research

Fundamentals of Developing a Water Quality Monitoring Plan

March 5, 2024

What are we covering?

Data Inventory: What data do you need, where to obtain it, best practices to maintain it.
Watershed Characterization: What is it and provide an overview of approaches.
Data Sufficiency: Do you have the data you need? Do you have enough data?

Inventory and Acquire Existing Data

Major steps

flowchart LR
  A(Determine data needs) --> one
  subgraph one[Create and maintain inventory]
    direction TB
    D --> B
    B(Identify available data) --> C(Locate the information)
    C --> D(Gather and organize data)
  end
  one --> E[(Data inventory)]

Determine data needs

While you are scoping and planning a project:

Identify stakeholder concerns
Identify watershed goals
Define conceptual models

Identify available data

Use existing data if/when available!
Assess the quality/reliability of that data.
Talk to organizations that have conducted projects in the area.

Locating information

What types of data will you need?

Watershed physical characteristics
Water quality
Streamflow
Climate
Pollutant sources

Locating information

Watershed physical characteristics

Watershed boundaries
Hydrographic/Topographic
Soil
Vegetation, wildlife, etc.

Locating information

Watershed physical characteristics

USGS NHD, NHD HR: https://www.usgs.gov/national-hydrography/access-national-hydrography-products
NLCD, Imperviousness, Tree Canopy, etc.: https://www.mrlc.gov/
NRCS Soil Survey:https://websoilsurvey.nrcs.usda.gov/app/

Locating information

Water quality

Routine water quality
Stormwater/runoff monitoring
Storm/flow biased water quality
Benthic and other stream surveys

Texas A&M AgriLife Marketing and Communications

Photo credit Michael Miller

Locating information

Water quality

TCEQ SWQM, Integrated Reports
CRP Partners
National Water Quality Portal (successor to STORET): https://www.waterqualitydata.us/
Hydroshare: https://www.hydroshare.org/
TPWD Bioassessment Studies
Texas Stream Team Citizen Science Data

Texas A&M AgriLife Marketing and Communications

Photo credit Michael Miller

Locating information

Streamflow

Daily/instantaneous streamflow
Inundation/tidal data

Texas A&M AgriLife Marketing and Communications

Photo credit Michael Miller

Locating information

Streamflow

National Water Information Service: https://waterdata.usgs.gov/nwis
Water Data for Texas: https://waterdatafortexas.org
Hydroshare: https://www.hydroshare.org/
NOAA: https://tidesandcurrents.noaa.gov/

Texas A&M AgriLife Marketing and Communications

Photo credit Michael Miller

Locating information

Climate

Precipitation
Temperature/solar radiation
Wind direction/speed

Texas A&M AgriLife Marketing and Communications

Photo credit Laura McKenzie

Locating information

Climate

NOAA Climate Data Online: https://www.ncei.noaa.gov/cdo-web/
OSU PRISM Climate Group: https://prism.oregonstate.edu/

Texas A&M AgriLife Marketing and Communications

Photo credit Laura McKenzie

Locating information

Pollution Sources

Permitted discharges
Non-permitted sources

Photo credit Ryan Gerlich (Texas A&M AgriLife Extension)

Locating information

Pollution Sources

EPA ECHO: https://echo.epa.gov/
TCEQ Water Quality General Permits: https://www2.tceq.texas.gov/wq_dpa/index.cfm
TCEQ Central Registry: https://www15.tceq.texas.gov/crpub/
Non-permitted sources: Often derived from secondary data sources (Census data, 911 addresses, wildlife surveys, etc.)

Photo credit Ryan Gerlich (Texas A&M AgriLife Extension)

Gather and organize data

Basic Principles:

Create a project specific data folder or database.
Maintain living project documentation.
Use data exploration and summary methods to get a “picture” of the data.

Gather and organize data

Documentation should identify:

Types of data (monitoring, geographic)
Source
Quality
Representativeness
Spatial coverage
Temporal coverage
Data gaps
Units and variable names

Your QAPP is a good starting point.

Gather and organize data

Best practices:

Consistent terminology and file naming (ex. Nitrate as nitrogen, NO₃-N, or nitrate…).
Standardized dates (highly suggest YYYY-MM-DD formatting).
Use non-proprietary files where possible (plain-text, csv, etc.).
What are your data storage practices?

Licensed under Creative Commons CC BY-NC 2.5 DEED

Image: https://xkcd.com/1459/

Gather and organize data

Spreadsheet best practices:

No empty cells: specify no value, NA, zero, infinity, etc.
One value per cell.
Do not use font color, cell color, or comments, as data.

Data Organization in Spreadsheets: https://doi.org/10.1080/00031305.2017.1375989

Watershed Characterization

Photo courtesy of Ed Rhodes

What is a Watershed Characterization?

An assessment of past and current conditions within a watershed to support water quality management decisions

What is a Watershed Characterization?

Can have different objectives:

Is there a (or where is the) water quality problem?
Compare and define current and desired conditions.
Estimate allowable loadings for point source permits or TMDLs
Evaluate and recommend best management practices
Assess trends or changes in water quality

(not an exhaustive list…)

How do we do it?

Depends on our objectives!

Objectives determine the statistical design and data (monitoring requirements)

General approaches

Reconnaissance
Plot studies
Watershed(s) study

Reconnaissance

Objective: Identify if or where there is a water quality problem.

AKA routine or ambient water quality
Provides a snapshot of water quality at a given point in time
Limited statistical application (summary statistics or probability of exceedance)
TCEQ assessments manual provides baseline methods for comparison.

Reconnaissance

Advantages:

You might already have enough data (CRP monitoring)
Large spatial coverage
Useful to pinpoint problematic areas

Reconnaissance

Disadvantages:

Biased towards baseflows
Possibly low sampling frequency in existing data
Sampling locations are biased

Plot studies

Objective: Evaluate differences in conditions or treatments

Experimental trials on multiple plots involving treatment and control replicates. Treatments are small plots where runoff can be routed to a single measurement point per treatment.

Image USDA NRCS (2003)

Plot studies

Often used to evaluate the impact of BMPs on runoff quality.

Advantages:

Well designed experiments have strong statistical power (reliability for detecting true effects)
Ability to control for environmental conditions

Plot studies

Disadvantages:

Cost
Conditions may not represent real world conditions
Results are sometimes (often?) condition specific

Plot studies

TWRI

Photo credit Jason Gerlich

Watershed studies

Objective: Evaluate Best Management Practices (BMPs), assess trends.

Multiple potential study designs:

Design	Causal factors
Single watershed - before/after	BMPs, Climate
Single watershed - upstream/downstream	BMP, within watershed factors
Paired watershed - BACI	BMP

Single watershed

Objective: Evaluate conditions before and after implementation, or trend analysis.

Single monitoring site at the outlet of a watershed.
Relatively inexpensive.
Difficult to separate effects due to changes climate or other watershed changes.

Image USDA NRCS (2003)

Single watershed

Objective: Evaluate conditions before and after implementation, or trend analysis.

Monitoring before and after implementation.
Monitor upstream and downstream of implementation.
Accounts for some differences within the watershed.
Disadvantage, downstream measurements are not fully independent of the upstream measurements.

Single watershed (above/below implementation.)

Image USDA NRCS (2003)

Paired watershed

Objective: Evaluate conditions before and after implementation, or trend analysis.

Monitor before and after in calibration and treatment watersheds
Two or more watersheds
One watershed always serves as a control

Image USDA NRCS (2003)

Paired watershed

Advantages:

Can be statistically powerful.
Can evaluate multiple treatments.
Helps control for environmentally induced variation not associated with treatment.
Statistical independence between treatment/pre-treatment measurements.
Regionally relevant results.

Paired watershed

Disadvantage:

Requires long study period.
Lag effects can be gradual masking difference with control (Meals, Dressing, and Davenport 2010).
Can be hard to find two watersheds that won’t have any other distrubrances for the entire study period.
Logistics.

Trends analysis

Objective: Assess a trend or change in water quality parameters at a single or multiple location.

Evaluate effect size over time.
Doesn’t require “reference” watersheds but can be helpful.

Image Helsel et al. (2020)

Trends analysis

Advantages:

Can use a single station.
Useful for practices with long-lag times.
Useful for assessing larger watersheds with many implementation activities.

Trends analysis

Disadvantages:

Typically requires long-term data (10-20 years).
High chance of changes in watershed and lab methodologies.

Sufficient data

Depends on:

Statistical design
Variability of measurement parameters
Relevent effect size

Sufficient data

Things to consider:

Adequate number of sites
Adequate number of samples
Frequency of samples
Monitoring duration

Sufficient data

Available tools:

TCEQ assessment criteria (for ambient water quality)
Statistical power analysis

Additional theme classes

Some extra things you can do with the txwri-revealjs theme

Special classes for emphasis

.alert class for default emphasis, e.g. important note.
.fg class for custom colour, e.g. important note.
.bg class for custom background, e.g. important note.

Components

Citations

Citations follow the standard Quarto format and be sourced from BibLaTex, BibTeX, or CLS files. For example:

Topic 1: Stats and trends (Helsel et al. 2020; Schramm, Gitter, and Gregory 2022)
Topic 2: Watershed governance (Biddle 2017)

Components

Blocks

Quarto provides dedicated environments for theorems, lemmas, and so forth.

But in presentation format, it’s arguably more effective just to use a Callout Block.

Regression Specification

The main specification is as follows:

\[ y_{it} = X_{it} \beta + \mu_i + \varepsilon_{it} \]

Components

Multicolumn I: Text only

Column 1

Here is a long sentence that will wrap onto the next line as it hits the column width, and continue this way until it stops.

Column 2

Some other text in another column.

A second paragraph.

Multicolumn support is very flexible and we can continue with a single full span column in the same slide.

Components

Multicolumn II: Text and figures

A point about the figure that is potentially important.
Another point about the figure that is also potentially important.

Note that sub- and multi-panel figures are also natively supported by Quarto. See here.

Components

Multicolumn III: Code and output

palette("Classic Tableau")

par(
  family = "HersheySans",
  las = 1, pch = 19, cex = 1.5
)

pairs(
  iris[,1:4],
  col=iris$Species
)

Figures

Figure

Photo courtesy of Ed Rhodes

Figure

Full-size Figures

You can use the {.background-image} container environment to completely fill the slide background with an image.

Ideally, your figure will be the same aspect ratio as the screen that you’re presenting on.

This can be a bit tricky because of the dynamic nature of reveal.js / HTML. But it’s probably something close to 16:9.
Aspect ratio can also matter for a regular full-frame images (previous slide).

Contact us

We’d love to talk about all things water

References

Biddle, Jennifer C. 2017. “Improving the Effectiveness of Collaborative Governance Regimes: Lessons from Watershed Partnerships.” Journal of Water Resources Planning and Management 143 (9): 04017048. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000802.

Helsel, Dennis R, Robert M Hirsch, Karen R. Ryberg, Stacey A. Archfield, and E J Gilroy. 2020. Statistical Methods in Water Resources: U.S. Geological Survey Techniques and Methods, Book 4, Chapter A3. Reston, VA: USGS. https://doi.org/10.3133/tm4a3.

Meals, Donald W., Steven A. Dressing, and Thomas E. Davenport. 2010. “Lag Time in Water Quality Response to Best Management Practices: A Review.” Journal of Environment Quality 39 (1): 85. https://doi.org/10.2134/jeq2009.0108.

Schramm, Michael, Anna Gitter, and Lucas Gregory. 2022. “Total Maximum Daily Loads and Escherichia Coli Trends in Texas Freshwater Streams.” Journal of Contemporary Water Research & Education 176 (1): 36–49. https://doi.org/10.1111/j.1936-704X.2022.3374.x.

USDA NRCS. 2003. “National Water Quality Handbook Part 614.” USDA NRCS. https://archive.epa.gov/water/archive/web/pdf/stelprdb1044775.pdf.

Extra slides

Types of data

Qualitative, quantitative, geospatial, temporal, etc.

Qualitative data

Historic documentation (newspaper articles, interviews)
Some types of metadata
Photographs
Comments field in SWQM data

Qualitative data

Provides sense of place
Why and when things changes

Spatial data

Remotely sensed gridded data Primarily land cover and elevation

Spatial data

Modeled gridded data Precipitation, ET/PET, solar radiation etc.

Useful inputs for watershed models (SWAT, etc.)

Spatial data

Topological data

Primarily stream networks and associated waterbodies

Quantitative data

Primarily interested in: - Streamflow/discharge - Discrete or continuous water quality